With the growing reliance on automated code completion tools in software development, the need for comprehensive evaluation benchmarks has become critical. Existing benchmarks focus more on code completion in function and class level by pro-viding text descriptions to prompt the model. By contrast, such descriptive prompt is commonly unavailable in real development and code completion can occur in wider range of situations such as in the middle of a function or a code block. These limitations makes existing evaluation benchmarks poorly align with the practical scenarios of code completion tools. In this paper, we propose RepoMasterEval, a novel benchmark for evaluating code completion models constructed from real-world repositories. Each benchmark datum is generated by masking a code snippet (ground truth) from one source code file with existing test suites. To improve test accuracy of model generated code, we employ mutation testing to measure the effectiveness of the test cases and we manually crafted new test cases for those test suites with low mutation score. Our empirical evaluation on 10 state-of-the-art models shows that test argumentation is critical in improving the accuracy of the benchmark and RepoMasterEval is able to report variance in model performance in real-world scenarios. The deployment of RepoMasterEval also revealed that the benchmark is useful to give accurate feedback during model training and the score is in high correlation with the model’s performance in practice.

In this work, we introduce CodeRepoQA, a large-scale benchmark specifically designed for evaluating repository-level question-answering capabilities in the field of software engineering. CodeRepoQA encompasses five programming languages and covers a wide range of scenarios, enabling comprehensive evaluation of language models.To construct this dataset, we crawl data from 30 well-known repositories in GitHub, the largest platform for hosting and collaborating on code, and carefully filter the raw data.In total, CodeRepoQA is a multi-turn question-answering benchmark with 585,687 entries. It covers a diverse array of software engineering scenarios, with an average of 6.62 dialogue turns per entry.

Large language models (LLMs) have revolutionized code completion tasks. IDE plugins such as MarsCode can generate code recommendations, saving developers significant time and effort. However, current evaluation methods for code completion are limited by their reliance on static code benchmarks, which do not consider human interactions and evolving repositories. This paper proposes RepoSim, a novel benchmark designed to evaluate code completion tasks by simulating the evolving process of repositories and incorporating user behaviors. RepoSim leverages data from an IDE plugin, by recording and replaying user behaviors to provide a realistic programming context for evaluation. This allows for the assessment of more complex prompt strategies, such as utilizing recently visited files and incorporating user editing history. Additionally, RepoSim proposes a new metric based on users' acceptance or rejection of predictions, offering a user-centric evaluation criterion. Our preliminary evaluation demonstrates that incorporating users' recent edit history into prompts significantly improves the quality of LLM-generated code, highlighting the importance of temporal context in code completion. RepoSim represents a significant advancement in benchmarking tools, offering a realistic and user-focused framework for evaluating code completion performance.